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Record W2944617763 · doi:10.1353/llt.2019.0004

“Maybe We Shouldn’t Laugh So Loud”

2019· article· en· W2944617763 on OpenAlexfundvenueaboutno aff
Shiva Nourpanah

Bibliographic record

VenueLabour / Le Travail · 2019
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsnot available
FundersDalhousie University
KeywordsPolitical sciencePoliticsEthnographyHealth careEthnologyHumanitiesSociologyArtAnthropology

Abstract

fetched live from OpenAlex

My research explores the labour conditions experienced by foreign nurses employed in health care in the province of Nova Scotia, Canada, on temporary permits. I draw on ethnographic interviews to understand the nuanced ways in which foreign nurses feel welcomed in their local communities and workplaces, yet simultaneously remain subject to hostile racialized scrutiny. Nova Scotia is one of the least ethnically diverse provinces in Canada and one of the most economically impoverished. It faces a shortage of healthcare workers, exacerbated by the ongoing restructuring of the healthcare sector. These contextual factors contribute to the complicated push-pull matrix discussed by the temporary foreign nurses, who feel needed, but not wanted. This matrix cannot be dismissed as simply the racism and “backwardness” of local communities. Rather, it must be understood through a political economy focus on temporary foreign workers, restructured health care, and the normalization of a precarious labour landscape in which racialized foreign and local workers are pitted against each other. Mes travaux de recherche portent sur les conditions de travail subies par les infirmiers et les infirmières étrangers de permis temporaire employés dans les soins de santé dans la province de la Nouvelle-Écosse, Canada. Je m’appuie sur des entretiens ethnographiques afin de saisir les façons nuancées dont les infirmiers étrangers se sentent bien accueillis dans leurs collectivités, et en même temps font encore l’objet d’un contrôle racialisé hostile. La Nouvelle-Écosse est l’une des provinces les moins diversifiées sur le plan ethnique au Canada, ainsi que l’une des provinces les plus économiquement démunies. Elle est en outre aux prises avec une pénurie de travailleurs de la santé, aggravée par la restructuration en cours dans le secteur de la santé. Ces facteurs contextuels contribuent à la matrice complexe du « pousser/tirer » examinée par les infirmiers étrangers temporaires qui se sentent nécessaires, mais non désirés. On ne peut écarter cette matrice comme s’il s’agissait simplement du racisme et du « retard » des collectivités locales. Elle doit plutôt être comprise en mettant l’accent à caractère de l’économie politique sur les travailleurs étrangers temporaires, les soins de santé restructurés, et la normalisation d’un marché du travail précaire dans lequel les travailleurs racialisés, étrangers et domestiques, sont dressés les uns contre les autres.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.473
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.009

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.038
GPT teacher head0.351
Teacher spread0.313 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations9
Published2019
Admission routes3
Has abstractyes

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